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. Author manuscript; available in PMC: 2022 Jul 23.
Published in final edited form as: Schizophr Res. 2020 Jan 7;216:85–96. doi: 10.1016/j.schres.2019.12.026

Effect of music listening on P300 event-related potential in patients with schizophrenia: A pilot study

Shikha Ahuja a,#, Rajnish Kumar Gupta b,#, Dinakaran Damodharan c, Mariamma Philip d, Ganesan Venkatasubramanian c, Matcheri S Keshavan e, Shantala Hegde a,*
PMCID: PMC7613152  EMSID: EMS150894  PMID: 31924375

Abstract

Reduced amplitude and increased latency of P300 auditory event-related potential (ERP) in patients with schizophrenia (SZ) indicate impairment in attention. Overall arousal level can determine the amount of processing capacity required for attention allocation. Music evokes strong emotions and regulates arousability. Music has been used to modulate P300, especially in normals. This exploratory study examined the effect of music listening on the amplitude and latency of P300 in SZ patients.

EEG/ERP was recorded (32-channels) while SZ patients (n = 20; 18–45 years) performed an auditory oddball P300 task after the eyes-closed rest condition (Condition-A) and ten-minute music listening condition (Condition-B) as per the complete counterbalancing design (AB-BA). Patients listened to the researcher chosen, instrumental presentation of raag-Bhoopali in the North-Indian-Classical-Music, for ten-minutes. All patients rated the music excerpt as a relaxing and positively valenced.

A significant increase in accuracy score and reaction time during the oddball task after music listening was noted. There was an increase in amplitude at TP7. A trend of increased amplitude was noted across all electrodes in the music condition compared to the rest condition. Mean amplitude in an apriori defined time window of interest (250 to 750 ms) showed significant changes in the frontal and central electrode sites. Power spectral analysis indicated a slight increase in frontal and central alpha and theta activity during music listening. However, this was not statistically significant.

Findings add further impetus to examine the effect of music in chronic psychiatric conditions. Need for systematic studies on a larger cohort is underscored.

Keywords: P300, Event relate potential, Schizophrenia, Attention, Indian classical music, Raga / Raag, North Indian classical music

1. Introduction

Cognitive deficits have been considered as a central feature of the neuropsychopathology in schizophrenia (SZ) (Heinrichs et al., 2013; Green and Harvey, 2014). Although cognitive impairment is often widespread in this debilitating clinical condition, deficits in vigilance are extensively studied as SZ patients show consistently poor performance in various tasks of attention (Nuechterlein, 1991; Fioravanti et al., 2005; Green, 2006; Nuechterlein et al., 2015). Deficits in attention are considered an enduring feature and interfere with successful social and occupational functioning (Harris et al., 2007; Morris et al., 2013). Meta-analytic studies have indicated that sustained attention is a core cognitive deficit of SZ related to problem-solving and skill acquisition (Nuechterlein et al., 2004). Importantly, attention deficits have been associated with various aspects of quality of life and global functioning in SZ patients (Green et al., 2000; Hegde et al., 2013; Nuechterlein et al., 2015). It is widely believed that the cause of narrowing of the attentional field in patients with SZ is due abnormalities in arousal, mostly due to the state of hyperarousal (Gruzelier, 1973; Gjerde, 1983; Nakamura et al., 2003; Dinzeo et al., 2008). SZ has been associated with both central and autonomic arousal abnormalities (Venables andWing, 1962; Zahn et al., 1981; Dawson et al., 1994; Schulz et al., 2016). EEG studies have reported that the desynchronization of alpha waves could be suggestive of the hyperarousal state observed in SZ (Fenton et al., 1980; Saletu et al., 1990; Dinzeo et al., 2008).

Neurophysiological measures such as electroencephalography (EEG) have been widely used with patients of SZ as means to assess the information processing underlying attention, allowing for a deeper understanding of this illness (Morstyn et al., 1983; Boutros et al., 2008). Resting-state brain activity in patients with SZ shows decreased alpha, increased beta, theta, and delta pattern (Boutros et al., 2008). Patients with SZ have also consistently shown abnormal activity in the gammaband (Uhlhaas, 2011). Event-related potential (ERP) is an objective index which can help us quantify the level of cognitive impairment in SZ. P300 or P3 is a commonly studied ERP reflecting various cognitive operations like selective attention, decision making, stimulus discrimination, attentional resource allocation, context updating, working memory (Donchin and Coles, 1988; Brumback et al., 2005; Polich, 2007; O’connell et al., 2012). The P300 latency reflects the speed of information processing whereas P300 amplitude is associated with the amount of resources allocated for processing (Polich, 2007). It is seen that shorter P300 latencies and larger amplitudes indicate superior information processing (Polich, 2004, 2007). Studies examining attention deficits in SZ using auditory oddball paradigm have shown reduced amplitude and longer latency of P300 (Jeon and Polich, 2003; Mathalon et al., 2010). The overall arousal level determines the amount of processing capacity needed for attention allocation, modulating the latency and amplitude of P300 (Polich, 2007). A state of hyperarousal is also considered as an indicator of vulnerability to psychosis (Clamor et al., 2015). Arousal predisposition is known to be high in patients with schizophrenia (Dinzeo et al., 2004; Docherty et al., 2008).

Music is a very strong elicitor of emotion and a powerful method to impact arousability in an individual (Blood and Zatorre, 2001; Rickard, 2004; Schellenberg et al., 2007; Salimpoor et al., 2009). It is also observed that musically trained individuals showed lower latencies and higher amplitudes than musically untrained individuals in the P300 task without contralateral noise (Rabelo et al., 2015). A range of studies has shown that music has the potential in enhancing a variety of cognitive functions, such as attention, learning, language and memory (Thompson et al., 2001; Schellenberg et al., 2007). A popular hypothesis to explain the impact of music on cognition is the arousal and mood hypothesis (Thompson et al., 2001). It states that improved cognitive performance could be attributed to the positive affect and heightened arousal state that can be induced by any pleasant musical or nonmusical stimulus (Hallam et al., 2002; Schellenberg and Weiss, 2013). It is also widely accepted that positive affect can broaden the scope of cognitive functioning and modulate performances on various tasks of visual and auditory attention (Fredrickson, 2004; Baumann and Kuhl, 2005; Putkinen et al., 2017). Evidence also suggests that instrumental music can improve auditory attention by modulating the mood of the participants (Putkinen et al., 2017). Previous studies have reported that an average of P300 amplitude was observed to be higher in Fz region in participants who found a given piece of music as pleasant versus those who found it unpleasant (Kayashima et al., 2017).

In the normal population, it has been observed that pleasant music is associated with an increased frontal midline (Fm) theta power (Sammler et al., 2007; Lin et al., 2010; Fachner et al., 2013). In addition, frontal alpha asymmetry is noted while listening to positive valence music (Schmidt and Hanslmayr, 2009). Only a handful of studies have examined the effects of listening to music by objective physiological measures in patients with SZ (Geretsegger et al., 2017). Music may increase alpha EEG activity and decrease beta EEG activity in patients with depression and SZ (Yang et al., 2012). Similarly, a recent study found that alpha waves were consistently present for a patient with SZ in music listening condition, indicating improved emotional relaxation (Kwon et al., 2013). In a study on patients with SZ beneficial effects of a single session of music listening on the Stroop task have been reported. This study was based on the arousal and mood hypothesis, considering that there is a close relationship between attentional deficit and hyperarousal among patients with SZ (Glicksohn et al., 1999). A few studies have examined the effect of music on P300 components in the non-clinical population (Arikan et al., 1999; Zhu et al., 2008; de Sá and Pereira, 2011; Gu et al., 2014). It was found that exposure to music altered the amplitude of P300, facilitating sustained attention in female subjects (de Sá and Pereira, 2011). Furthermore, a recent study found that the amplitude of P300 component during the performance N-Back test improved after listening to Baroque music and that there was an increase in the alpha waves post music listening (Gu et al., 2014). Music played with the ney (ney-flute) an instrument, which was familiar to the Turkish participants in a study elicited larger P300 amplitudes than compared to the music played on a lesser familiar instrument, violin/cello (Arikan et al., 1999). Along the same lines, Zhu et al. (2008) have reported that music played to Chinese participants in their study with ‘guqin’, a culturally familiar instrument, elicited a stronger amplitude P300 in the frontal region than music played with piano. However, the influence of music on the latency of P300 is less noted in most of these studies (Arikan et al., 1999; de Sá and Pereira, 2011). The above-mentioned studies provide research evidence that listening to music can have a positive effect on memory and attentional resources. Understanding of neural correlates underlying such effect of music on attention and other cognitive functions are still limited. Emotional processing evoked by music is associated with changes in the activity of limbic and paralimbic structures such as amygdala, nucleus accumbens, and anterior cingulate cortex. Positive emotional states induced by pleasurable music have been associated with dopamine release in the reward pathway (Salimpoor et al., 2011). Fm theta power changes have been associated with the activity in the anterior cingulate cortex (ACC) (Ishii et al., 1999; Pizzagalli et al., 2003). Music therapy sessions have been reported to bring about consistency in alpha brain wave changes in SZ (Kwon et al., 2013). The benefits of music therapy as an adjunct therapy or given along with specific cognitive remediation methods in SZ have been gaining importance in the recent past (Geretsegger et al., 2017; Kosugi et al., 2019). A recent meta-analysis study that included twelve published articles on music therapy for SZ, has indicated that music as an adjunct therapy has a significant impact on positive symptoms, negative symptoms and mood symptoms regardless of the total duration or duration of each session, frequency and a total number of sessions (Tseng et al., 2016). The effects of group music therapy sessions on cognitive functions, symptom profile, on mood as well as in bringing about consistency in alpha brain waves have been reported (Ulrich et al., 2007; Peng et al., 2010; Kwon et al., 2013; Lu et al., 2013).

To the best of our knowledge, no study yet has examined the effect of one-time music listening on P300 in patients with SZ, a debilitating clinical condition with deficits in attention and other cognitive functions. The aim of the current exploratory study was to investigate whether music listening can modulate the amplitude and latency of P300 in patients with SZ and no study hitherto has been carried out in this clinical condition. So far, music therapy in schizophrenia has been predominantly carried out from a social science model targeting overall mental health, quality of life as well as positive and negative symptoms and not much from a neuroscience model targeting cognitive functions (Talwar et al., 2006; Geretsegger et al., 2017).

The findings from this pilot study can be considered as an evidence to target the cognitive deficits in patients with SZ using music-based cognitive remediation. It can also extend our knowledge about neural underpinnings and neural mechanisms underlying music and attention.

2. Materials and methods

2.1. Sample

The sample included 20 right-handed male SZ patients, aged between 18 and 45 years. They were recruited from the out-patient and in-patient clinical services of the Institute. The diagnosis was made as per the ICD-10 criteria for SZ by the two psychiatrists in the team. In the total sample, 18 were diagnosed with paranoid SZ and 2 were diagnosed with undifferentiated SZ. Patients with a score of less than the cut-off of 24 on the Hindi Mental State Examination (HMSE) were screened out (Lezak, 1984; Ganguli et al., 1995). Patients with acute psychotic symptoms or any secondary diagnosis of medical/neurological, patients with family history of any severe mental illness in first degree relations, substance abuse or dependence, patients who had undergone electroconvulsive therapy (ECT) in the past 6 months and those with formal training more than two years in any form of music were excluded from the study. Eight patients were in their early phase of the illness i.e. equal to or less than ≤5 years (mean duration = 3.31 years, SD = 1.4 years) and 12 participants had an illness duration of more than 5 years (mean duration = 12.48, SD = 3.52). All the patients were on regular antipsychotic medication during recruitment. The chlorpromazine equivalent dose was calculated from the dose of antipsychotics that each of the patients was receiving (Table 1). All patients reported that they listened to music (7.8 h per week) and listened to mainly film music (Bollywood music). Only three patients reported that they listened to Indian classical music and another genre of music (Table 2).

Table 1. Socio-demographic and clinical information of the sample (n = 20).

Variable Score ± S.D.
Age (years) 31.30 ± 7.64
Formal Education (years) 13.58 ±2.78
Age of onset (years) 22.20 ± 4.30
Duration of illness (years) 9.03 ± 5.58
Current CPZ equivalent (mg) 565.79 ± 312.27
HMSE 27.55 ± 1.50
BPRS 36.58 ± 8.87
SAPS 33.26 ± 19.81
SANS 38.95 ± 14.19
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(CPZ: Chlorpromazine; HMSE: Hindi Mental Status Examination; BPRS: Brief Psychiatric Rating Scale; SAPS: Scale for the Assessment of Positive Symptoms; SANS: Scale for the Assessment of Negative Symptoms)

Table 2. Music Background details of the sample (n = 20).

Variable
Average number of hours per week spent in listening to music 7.8
Preferred style of music
Bollywood frequency (%) 17 (85)
English pop frequency (%) 1 (5)
Ghazal frequency (%) 1 (5)
Indian classical frequency (%) 1 (5)
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2.2. Study design

After the initial screening, patients were evaluated on the HMSE (Ganguli et al., 1995). The music listening questionnaire and Edinburgh Handedness Inventory (Oldfield, 1971) were then administered. Patients who provided the written informed consent were taken further for the study. Psychopathology ratings were carried out by the psychiatrist using the Scale for Assessment of Positive & Negative Symptoms (SAPS & SANS) (Andreasen, 1983) and the Brief Psychiatric Rating Scale (BPRS) (Overall and Gorham, 1962) a day prior to the EEG/ERP part of the study. Instructions to follow for EEG recording were then provided (such as requesting the patient to wash hair to remove oil content on the scalp, refrain from any alcohol consumption 24 h prior to the recording and caffeine consumption prior to the EEG recording). The EEG recording was scheduled at different times of the day for each patient depending upon their medication time and taking into consideration when the patient reported as being most alert and not drowsy.

The experiment was conducted in a dimly-lit, sound-attenuated room. After placement of the EEG cap (lycra stretch cap Quick Cap, Neuroscan Inc.), participants were given instructions for the task. Prior to recording, the participant performed a block of 10 practice trials, to ensure that the patients understood the instructions. Reaction time and accuracy during the performance were equally stressed upon. To examine the effect of music listening on the components of auditory P300 in patients with SZ, 32-channel EEG/ERP was recorded while patients performed an auditory oddball P300 task after eyes closed rest condition (Condition A) and a ten-minute music listening condition (Condition B). The complete counterbalancing design was used in this study. Auditory P300 was recorded in the order of AB for ten patients and in the order of BA for ten patients (Table 3). During the auditory oddball paradigm, patients responded to the randomly occurring infrequent auditory stimuli (2000 Hz) by pressing a key, amidst the frequent auditory stimuli (1000 Hz). The occurrence of frequent infrequent stimuli ratio was 80:20. During the entire recording, session patients were asked to keep their eyes closed, and the EEG was recorded. The total duration of the entire experiment was approximately 30 min. The music and auditory P300 paradigm were presented using the 2.1 Bose speakers at a comfortable level of volume.

Table 3. Explaining the complete counterbalancing (AB-BA) design used in the study.

A B
Patients (n = 10) Rest P300 Music P300
All patients kept their eyes closed during the entire experiment
B A
Patients (n = 10) Music P300 Rest P300
All patients kept their eyes closed during the entire experiment
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Post experiment, participants were asked to fill the music feedback questionnaire to provide their subjective rating about their mood and arousal while listening to music. The subjective feeling/feedback about the music was collected using a Likert scale. Patients were then debriefed about the purpose of the experiment. It was also ensured that the caregivers accompanying the patients were psycho-educated about the illness before ending the session. The study protocol was reviewed and approved by the Institute's Ethics Committee.

2.3. EEG/ERP recording

The patient was seated comfortably while the EEG was recorded in the AC mode using the Neuroscan (Syn Amps) from 32 channels, passive electrodes, using the lycra stretch cap (Quick Cap, Neuroscan Inc.) with a sampling frequency of 1 kHz. A notch filter of 50 Hz was used. To control for eye movement artifacts, horizontal and vertical electrooculograms (EOG) were recorded bipolarly. Electrodes placed on the outer canthus of each eye (horizontal EOG) and one above and below the right eye (vertical EOG). One electrode each was placed on mastoids as linked ears reference. Electrode impedance was ensured less than 10 Kilo Ohms at each site.

2.4. Auditory P300 paradigm

The auditory P300 paradigm was developed using the Neuroscan Stim software (version 2.2). A standard oddball paradigm was used, in which patients were asked to press the button on the response pad (using the index finger of right hand) for the auditory target stimuli of 2000 Hz (infrequent stimuli, probability: 0.20) and to ignore standard stimuli of 1000 Hz (frequent stimuli, probability: 0.80). All stimuli were 200 milliseconds (ms) in duration with the inter-stimulus interval being 2000 ± 200 ms, presented randomly. The total number of stimuli was 250 with 50 targets and 200 distracters. Patients were instructed to keep their eyes closed for the entire experiment duration. The target stimuli were randomly presented in each trial. Patients were instructed to press the response button as quickly as possible when they heard the infrequent stimuli.

2.5. Music stimulus

The music stimulus chosen for the study was instrumental rendition in Hindustani Classical Raag, Bhoopali/Bhoop. Raag Bhoopali is a pentatonic scale consisting of the notes (Sa, Re, Ga, Pa, and Dha/Do Re Mi So La). It belongs to ‘Kalyan’ Thaat (Thaat-parent scale). This raag is known to evoke positively valanced emotion in its listeners (Nayar, 1989; Hegde et al., 2012). The selected music recording of highly accomplished Hindustani/North Indian Classical Musicians-Pandit Shivkumar Sharma and Pandit Hariprasad Chaurasia, recorded in Gat (composition) in- Jhaptaal (10 –beat rhythmic cycle) in madhyalaya (medium tempo), was a confluence of various musical instruments-Bansuri (Bamboo flute), Santoor (a 100 stringed instrument), table and pakhawaj (North Indian Percussion Instruments) and taanpura (drone) (Details of the recording from which the excerpt was chosen is provided at the end of the manuscript). The stimulus was shortlisted on the basis of previous research and feedback from a previous study carried out on musically untrained individuals (Hegde et al., 2012). The current study used a researcher-selected music stimulus. Researcher/Clinician chosen music has been reported to have a greater effect on stress reduction than music stimuli selected by the subjects themselves (Pelletier, 2004). Various other researches show that physical performance or psychological responses do not vary according to the researcher-selected and patient-selected music (Terry et al., 2014). Further, with a single music stimulus, it was possible to minimize the involvement of various psychological factors of music (familiarity, preference, rhythm, tone, tempo, etc.) which can impact the physiological arousability (Dean et al., 2011).

2.6. EEG/ERP data analysis

Analyses of the EEG/ERP data were carried out using Neuroscan software (Scan version 4.5). EEG data were first pre-processed by marking for eye movement and other motor artifacts manually from the raw EEG data. To remove these artifacts, spatial filter transformation was performed through principal component analysis (PCA) using singular value decomposition (SVD). Finite Impulse Response (FIR) bandpass-filter from 0.05 to 100 Hz with a zero-phase shift at 12db/octave, retaining all frequencies relevant for later analyses was applied. Then, epochs of 1100 ms were extracted for the correct infrequent targets, starting 100 ms before the stimulus onset of the standard and deviant events. A baseline correction procedure was done using the first pre-stimulus 100 ms. For P300 analysis, the largest positive peak occurring within 250–750 ms post-stimulus presentation was identified as the P300 peak.

For power spectral analysis, the artifact-free data were epoched using 1024 data points. The analysis was performed on continuous data while listening to music and while at rest. The signals of all 30 electrode positions underwent the Fast Fourier Transformation (FFT) 0.5-s epochs with a Hanning window of 1024 Hz. Data were analyzed from 0.5 to 100 Hz, resulting frequency spectra were divided into four frequency bands: Theta: 4–8 Hz; Alpha: 8–14 Hz; Beta: 14–35 Hz; Gamma: 35 Hz and above (Newson and Thiagarajan, 2018).

Further statistical analyses were carried out after testing all values of interest for normality using the Shapiro-Wilk test (Shapiro and Wilk, 1965). For the data group that violated the normality assumption, the non-parametric paired Wilcoxon Signed Rank test was used to compare P300 amplitude, latency and power spectrum in music and rest condition. For the values that were normally distributed, the paired t-test was used. The significance threshold of the group differences was set to p < .05.

3. Results

3.1. Subjective evaluation of the music

All except one patient reported that the music presented was expressing a peaceful emotion. One patient reported that the musical excerpt was expressing a happy emotion. Self-rating for the arousing nature of music showed that 53% considered the musical piece as soothing and very soothing. Eleven percent of participants found the music to be very energizing and 24% of participants rated the music to be neutral in terms of arousability.

3.2. Accuracy scores on the P300 task

A total number of hits (correctly detecting the infrequent target) and correct rejection together constituted the total accuracy score. In other words, the total number of times patients pressed the response button using the right-hand index finger when the infrequent stimuli (2000 Hz) was presented and the number of times the patient did not press the button when the frequent stimuli (1000 Hz) was presented constituted the accuracy score. Participants gave approximately 98.4% of total correct responses, with approximately 90% of hits and 0.09% of false alarm in the music condition. In the rest condition, participants gave approximately 97%of total correct responses with 86% of hits and 0.28% of false alarm. There was higher accuracy in the music condition than the rest condition; t (18) = 2.48, p = .023. Reaction time (RT) was significantly shorter in the music condition as compared to the rest condition, t (18) = 2.21, p = .041 (Table 4).

Table 4. Comparison between accuracy and reaction time (RT) in music and rest conditions.

Mean ± S.D. Paired t-test
value		 p-value
Music Rest
Accuracy 245.74 ± 10.43 243.21 ± 11.12 2.48 0.023*
RT (milliseconds) 1726.19 ± 96.90 1741.35 ± 97.49 -2.21 0.041*
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3.3. ERP analysis

The data from one subject was removed from further analysis due to significant noise in the recording in all the channels. ERP waveforms were calculated for only infrequent correctly detected targets. For each electrode, the amplitude values were averaged across the participants. To examine the regional differences, electrodes were grouped into five different brain regions. A score for each region was calculated by averaging the scores of the individual electrodes in that region (Fig. 1).

Fig. 1. 32- Electrodes used in the present study- as per the 10–20 system.

Fig. 1

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Changes observed in the amplitude and latency measurements after music condition (Z value and P values) are reported in Table 5. A significant change in amplitude was observed at TP7 (Z = 2.02, p = .044) (Fig. 2). A trend of increased amplitude was noted across all electrodes in the music condition as compared to the rest condition (Fig. 3). Percentage change of amplitude between music and rest conditions indicates a marked change in F8, FP2, and FT8 (Fig. 4).

Table 5. Results of the comparison of the characteristics of P300 (amplitude and latency changes) observed between rest and music conditions (Wilcoxon signed-rank test).

Electrode site Amplitude Latency
Z-value p-value Z-value p-value
O2 1.408 0.159 –1.771 0.08
O1 0.644 0.520 –1.147 0.25
OZ 0.966 0.334 –1.167 0.24
PZ 1.328 0.184 – 0.241 0.81
P4 1.288 0.198 –1.409 0.16
CP4 1.087 0.277 0.503 0.61
P8 0.684 0.494 0.201 0.84
C4 1.167 0.243 1.489 0.14
TP8 0.765 0.445 1.59 0.11
T8 1.408 0.159 1.207 0.23
P7 1.207 0.227 –2.093 0.04*
P3 0.121 0.904 –0.181 0.86
CP3 1.006 0.314 –0.282 0.78
CPZ 0.966 0.334 –0.865 0.39
CZ 1.328 0.184 –0.986 0.32
FC4 0.885 0.376 1.982 0.05*
FT8 1.408 0.159 2.415 0.02*
TP7 2.012 0.044* –1.525 0.13
C3 1.006 0.314 0.161 0.87
FCZ 0.845 0.398 0.24 0.81
FZ 0.724 0.469 0.479 0.63
F4 1.328 0.184 0.704 0.48
F8 1.771 0.077 2.213 0.03*
T7 1.408 0.159 –2.314 0.02*
FT7 0.966 0.334 0.423 0.67
FC3 1.891 0.059 0.805 0.42
F3 1.207 0.227 0.483 0.63
FP2 1.006 0.314 0.362 0.72
F7 1.087 0.277 – 0.04 0.97
FP1 0.966 0.334 –0.196 0.84
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Fig. 2. Amplitude changes observed represented in waveform and scalp topography in the TP7 electrode site during music and rest conditions.

Fig. 2

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Fig. 3. Mean amplitude across 30 electrodes during music and rest conditions.

Fig. 3

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Fig. 4. Percentage change in amplitude across 30 electrodes.

Fig. 4

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We also further calculated the mean amplitude in an a priori defined time window of interest (250–750 ms) and significant mean amplitude changes were observed in the CP4, C4, T8, CZ, FC4, F4, and F8 electrode sites (Table 6, Fig. 5).

Table 6. Showing the mean amplitude changes occurred in 250 to 750 ms time window (Wilcoxon Signed Rank test).

Electrode site Amplitude
Z-value p-value
O2 1.449 0.147
O1 0.211 1.000
OZ 0.443 0.658
PZ 1.851 0.064
P4 1.932 0.053
CP4 2.334 0.020*
P8 1.61 0.107
C4 2.535 0.011*
TP8 1.73 0.084
T8 2.656 0.008*
P7 0.845 0.398
P3 0.885 0.376
CP3 1.73 0.084
CPZ 1.529 0.126
CZ 2.374 0.018*
FC4 2.857 0.004*
FT8 2.374 0.018*
TP7 1.851 0.064
C3 1.851 0.064
FCZ 1.73 0.084
FZ 0.966 0.334
F4 2.093 0.036*
F8 2.415 0.016*
T7 1.449 0.147
FT7 0.201 0.841
FC3 1.167 0.243
F3 0.201 0.841
FP2 0.805 0.421
F7 0.402 0.687
FP1 0.322 0.748
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Fig. 5. Mean amplitude in an apriori defined time window of interest (250-750 ms) across the 30 electrodes.

Fig. 5

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The results did not show any consistent pattern of change in the latency scores across electrodes in the two conditions. However, at the P7 electrode, the median latency significantly decreased from 471 ms in a rest condition to 445 ms in music condition; Z = 2.093, p = .036. At T7 electrode also there was a significant reduction in the latency from a median latency of 461 ms to 428 ms, Z = 2.273, p = .023. A significant increase in the latency was noted at electrode FT8 (Z = 2.012, p = .044) and FC4 (Z = 2.172, p = .03) in the music condition as compared to the rest (Fig. 6). A similar trend is observed when the data points were examined as a percentage change of latency between music and rest conditions (Fig. 7).

Fig. 6. Mean latency across 30 electrodes during music and rest conditions.

Fig. 6

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Fig. 7. Percentage change in latency across 30 electrodes.

Fig. 7

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3.4. Power spectrum analysis

A pattern of a slight increase in frontal and central alpha and theta activity while music listening was noted, although not statistically significant. At the occipital site, the alpha activity appeared to be reduced while music listening (Figs. 8, 9).

Fig. 8. Power spectral analysis-EEG power changes observed in the four bandwidths (Theta, Alpha, Beta, and Gamma) in the five brain regions (Frontal, Central, Temporal, Parietal and Occipital).

Fig. 8

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Fig. 9. Scalp topography of the depicting findings of the power spectral analysis between music and rest conditions.

Fig. 9

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4. Discussion

The present exploratory study was carried out with the aim of examining the effect of music listening on the auditory P300 in patients with SZ. Patients were assessed on auditory oddball P300 task following a rest condition and following music listening condition to measure the changes in the latency and amplitude of P300.

The results of the study indicate that after listening to the researcher-chosen music excerpt which lasted for ten minutes, the accuracy on the auditory P300 task increased significantly and the reaction time decreased for the patients with SZ. Behavioral responses seem to have been facilitated by music, indicating an increased attentional effort after listening to music. Previous research studies on healthy population point out the similar positive effect of music listening on various performance variables such as error rate, accuracy and reaction time (Zakharova and Ivashchenko, 1984; Hallam et al., 2002; Chraif et al., 2013; Schellenberg and Weiss, 2013). This result is also in line with the previous study showing the beneficial effect of one session of music listening was seen on the performance outcome variables of error rates and naming latency in the Stroop task. The changes in attentional deficits to the “reduction in arousal” of already hyper-aroused SZ patients (Glicksohn et al., 1999; Nakamura et al., 2003). Venables and Wing (1962) using skin conductance responses concluded that chronic and more withdrawn schizophrenics are over-aroused. Additional evidence demonstrated a high level of activation in both acute and chronic patients using skin conductance in SZ (Zahn, 1964). For several decades, hyperarousability has been considered as an important factor in understanding attentional deficits in this clinical condition. The magnitude of attention deficits is thought to reflect high levels of arousal, a characteristic of SZ (Gjerde, 1983).

In the present study, although a maximum number of patients reported their preferred genre to be Bollywood music and did not have much exposure to raags of North Indian or Hindustani Classical music such as the one chosen in the study, more than half of them perceived the presented musical excerpt as soothing and evoking happy emotion. Self-report methods are considered “the best and most natural method to study emotional responses to music” (Gabrielsson, 2001). It can be said that the music piece selected modulated the arousal state of the patients impacting their mood, further facilitating performance on the cognitive task. Thus, the emotional arousal evoked by music in our study supports the mood-arousal hypothesis (Thompson et al., 2001). In patients with middle cerebral artery stroke, it is found that an increased level of arousal and positive emotions experienced by listening to music improves cognitive performance (Särkämö et al., 2008). Music that is considered to be pleasant has shown to bring about changes in the amplitude of the P300 in healthy participants specifically in the Fz region (Kayashima et al., 2017).

An important finding of the present study was the increased amplitude in the music condition in all electrode sites and the mean amplitude changes were observed predominantly in the frontal and central region. The P300 amplitude is associated with the amount of resources allocated for processing (Polich, 2007). Our results indicate that prominently larger amplitudes of P300 occurred after listening to music in the temporoparietal (TP7) region. The finding shows that there seems to be a promising trend towards improved cognitive processing and improved ability to allocate cognitive resources after listening to music. It has been found that the overall arousal level determines the amount of processing capacity needed for attention allocation, modulating the amplitude of P300 (Polich, 2007). This result replicates and extends the findings of the previously mentioned study, by incorporating the component of physiological measure (Glicksohn et al., 1999). Significant reduction in the latency was evident at temporal (T7) and parietal (P7) site whereas the latency was found to be increased at the frontal region (FC4 and FT8). This is in line with various studies, where authors have reported inconclusive findings for latency change after music listening (Zhu et al., 2008; de Sá and Pereira, 2011). Reaction time is associated with stimulus evaluation, response selection, and motor execution (Doucet and Stelmack, 1999; Polich, 2007). However, the latency of P300 is considered as an index of classification speed which is independent of response-related processes (Donchin and Coles, 1988; Doucet and Stelmack, 1999; Polich, 2007). Thus, changes induced by a single session of music listening seems to facilitate faster cognitive processing speed.

Power spectrum results show a pronounced trend of increased activity of alpha waves at the frontal and central region while listening to music. It is considered that alpha waves occur mostly in the conditions of mental inactivity and is inversely related to the cortical activity (Klimesch, 1999; Niedermeyer, 2005). In our study, the reduction of subjective arousal as reported by the patients could have facilitated them in allocating more resources for the attention-demanding task by reducing their physiological arousal as echoed by the increased alpha oscillations. This also indicates the relaxing effect of the chosen music piece. This finding confirms previous results showing the increased alpha oscillations in the frontal region while listening to music (Fachner et al., 2013). The findings from the current study are congruent with previous research with patients with SZ mentioned above. In a study by Kwon et al. (2013), multiple sessions of music listening have been found to elicit alpha waves and improve cognitive functioning as measured using Mini-Mental Status Examination in patients with chronic SZ (Kwon et al., 2013). Sammler et al. (2007) observed an increase in frontal midline theta power while listening to positive music stimuli. Increased frontal midline theta is also indicative of increased cognitive and executive functioning such as working memory and sustained attention tasks (Schacter, 1977; Jensen and Tesche, 2002; Hsieh and Ranganath, 2014). In our study, the pattern of increased frontal midline alpha and theta oscillations is suggestive of a relaxed state due to positive emotions evoked by the music stimuli. The findings further underscore the underlying mechanism of hyperarousal which could be affecting the attentional processes in SZ.

Changes in cognition induced by the music stimuli might be mediated through various brain structures. Theta waves originate predominantly from the hippocampus (Klimesch, 1999). The frontal midline theta is associated with the anterior cingulate cortex (Ishii et al., 1999; Pizzagalli et al., 2003). Both of which are known to have a prominent role in emotion processing (Koelsch, 2010). It is also well known that hippocampal volume reduction is seen in schizophrenia (Arnold et al., 2014). The emotional arousal evoked by music is also closely related to the dopaminergic activity, which is found to be dysregulated in SZ (Grace and Gomes, 2018). It is believed that alpha activity is modulated by the thalamus, which further regulates the activity of the frontal cortex and plays an important role in cognition (Feige et al., 2005). This suggests the common underlying mechanism of music and attentional processing.

No study to date has employed these variables to investigate the effects of a one-time music listening session on attention in SZ. As mentioned previously in the text, several studies have so far examined the benefits of music therapy sessions on psychopathology profile, mood, and brain wave (Geretsegger et al., 2017; Kwon et al., 2013; Tseng et al., 2016; Kosugi et al., 2019). To the extent reviewed, there is no study on music listening on P300 in SZ. The strength of this study lies in the carefully designed methodology. The 10 min P300 task consisted of adequate frequent and infrequent targets. The sample chosen was homogenous in terms of the diagnosis chosen. The music excerpt chosen was a single standardized stimulus controlling to a large extent the possible influence of various acoustic characteristics or subjective associations related to familiar music which could affect the physiological arousal. The patients included in the study did not have any previous musical training thereby controlling for any confounding factors due to formal training in music which is known to have beneficial factors with regard to cognitive functions (George and Coch, 2011; Rabelo et al., 2015).

There could be several possible reasons for the lack of an observed significant effect in the power values. It is possible that the relaxation induced by the music was weak to have a significant influence on alpha waves. Even though the music used in the present study has been shown to evoke emotion positive with valence (Hegde et al., 2012), certain studies show the influence of preference than relaxation effect produced by music listening plays a greater effect in generating alpha waves power (Hurless et al., 2013). It is also possible that longer and multiple sessions of music listening are perhaps required to enhance their relaxation level significantly. This study could further be extended by using the patient preferred music piece as well.

There are obvious limitations to this study. One, the sample consisted of only male patients with SZ with varied durations of illness and varying age of onset of illness. There was no matched healthy control group due to time constraints and a stipulated time period in which the study was conducted. Additional cognitive evaluation such as detailed neuropsychological evaluation was not carried out. Literature has very few studies about P300 and music and none exists in the SZ population. For this reason, the present study should be considered exploratory. Future studies, addressing the limitations stated, will help in investigating the effects to a better extent. It is further recommended that first-episode drug-naive patients, both genders, comparison of early and late-onset of illness to be studied for a more in-depth understanding of the effect of music on P300 in SZ. Comparison with education, gender, and age-matched healthy control group will add to the strength of future studies. It will be interesting to see if future studies can introduce an objective method to measure changes in physiological variables such as heart rate, galvanic skin response, especially keeping in mind the arousability theory and attention deficits in SZ.

It is well known that patients with SZ have smaller P300 as compared to healthy normal (Jeon and Polich, 2003). Therefore, the finding of increased P300 amplitude also suggests a normalization of the abnormal neural activity related to attention. The study throws light on improved behavioral performances and electrophysiological changes after one session of music listening in patients with SZ. The present study is only an exploratory study. Future studies addressing the limitations mentioned will have important implications for building the scientific evidence for music-based interventions in SZ targeting the cognitive deficits, especially attention deficit. There is undoubtedly a great need for integrated intervention programs to improve cognitive functions in SZ. A previous study has reported that adherence to music intervention in schizophrenia is far more compared to traditional drilled based cognitive exercises (Geretsegger et al., 2017). The present study is an exploratory, pilot study. Future research on similar lines on larger cohort is definitely imperative. Systematic studies are required to examine the effect of music-based interventions using the neuroscience model of music therapy such as Neurologic Music Therapy to improve cognitive impairments as well as social and emotional domains of functioning (Thaut, 2005; Koelsch, 2009; Hegde, 2014, 2017).

Musical excerpt

Album: The Valley Recalls Volume one: Live recordings from a concert given at the Nehru Center, Mumbai, December 1995. Navras Records Limited

Artists: Pandit Shivkumar Sharma (Santoor); Pandit Hariprasad Chaurasia (Bamboo Flute); Pandit Anindo Chatterjee (Tabla); Pandit Bhavani Shankar (Pakhawaj)

CD code: NRDVD 005; Website:https://www.navrasrecords.com/

Duration of the recording chosen for the present study: From 36 min 14 s to 46 min 14 s.

Acknowledgement

The authors thank the patients who gave consent to participate in this study.

Thanks to Mr. Deepak R Ullal, Senior Technician, Clinical Neuropsychology and Cognitive Neuroscience Center for providing the required technical support during the EEG/ERP recordings.

Footnotes

Authors contributions

Study Design: SH; Data Collection: SA, Data Analyses: RG, SH, SA, and MP; Clinical Screening and Clinical Evaluation of patients: GV and DD, Writing the manuscript: SH, SA, and RG; Revision: SH, RG, and MSK.

Disclosure

Corresponding Author SH is a Clinical and Public Health- 2018 Intermediate Fellow of the Wellcome Trust-DBT India Alliance (IA/CPHI/17/1/503348). Author GV acknowledges the support of Swarnajayanti fellowship by the Department of Science and Technology (DST/SJF/LSA-02/2014–15).

Declaration of competing interest

None.

References

  1. Andreasen N. Scale for the assessment of positive symptoms (SAPS) and scale for the assessment of negative symptoms (SANS) Handbook of Psychiatric Measures; 1983. [Google Scholar]
  2. Arikan MK, Devrim M, Oran Ö, Inan S, Elhih M, Demiralp T. Music effects on event-related potentials of humans on the basis of cultural environment. Neurosci Lett. 1999;268:21–24. doi: 10.1016/s0304-3940(99)00372-9. [DOI] [PubMed] [Google Scholar]
  3. Arnold SJ, Ivleva EI, Gopal TA, Reddy AP, Jeon-Slaughter H, Sacco CB, Francis AN, Tandon N, Bidesi AS, Witte B. Hippocampal volume is reduced in schizophrenia and schizoaffective disorder but not in psychotic bipolar I disorder demonstrated by both manual tracing and automated parcellation (FreeSurfer) Schizophr Bull. 2014;41:233–249. doi: 10.1093/schbul/sbu009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Baumann N, Kuhl J. Positive affect and flexibility: overcoming the precedence of global over local processing of visual information. Motiv Emot. 2005;29:123–134. [Google Scholar]
  5. Blood AJ, Zatorre RJ. Intensely pleasurable responses to music correlate with activity in brain regions implicated in reward and emotion. Proc Natl Acad Sci. 2001;98:11818–11823. doi: 10.1073/pnas.191355898. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Boutros NN, Arfken C, Galderisi S, Warrick J, Pratt G, Iacono W. The status of spectral EEG abnormality as a diagnostic test for schizophrenia. Schizophr Res. 2008;99:225–237. doi: 10.1016/j.schres.2007.11.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Brumback CR, Low KA, Gratton G, Fabiani M. Putting things into perspective: individual differences in working-memory span and the integration of information. Exp Psychol. 2005;52:21–30. doi: 10.1027/1618-3169.52.1.21. [DOI] [PubMed] [Google Scholar]
  8. Chraif M, Burtaverde V, Cojocaru AA. The effects of music exposure in time reaction to multipli stimuli. Romanian Journal of Experimental Applied Psychology. 2013;4 [Google Scholar]
  9. Clamor A, Warmuth AM, Lincoln TM. Arousal predisposition as a vulnerability indicator for psychosis: a general population online stress induction study. Schizophr Res Treat. 2015;2015 doi: 10.1155/2015/725136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Dawson ME, Nuechterlein KH, Schell AM, Gitlin M, Ventura J. Autonomic abnormalities in schizophrenia: state or trait indicators? Arch Gen Psychiatry. 1994;51:813–824. doi: 10.1001/archpsyc.1994.03950100061006. [DOI] [PubMed] [Google Scholar]
  11. De Sá CI, Pereira LD. Musical rhythms and their influence on P300 velocity in young females. Braz J Otorhinolaryngol. 2011;77:158–162. doi: 10.1590/S1808-86942011000200004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dean RT, Bailes F, Schubert E. Acoustic intensity causes perceived changes in arousal levels in music: an experimental investigation. PLoS One. 2011;6:e18591. doi: 10.1371/journal.pone.0018591. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Dinzeo TJ, Cohen AS, Nienow TM, Docherty NM. Stress and arousability in schizophrenia. Schizophr Res. 2004;71:127–135. doi: 10.1016/j.schres.2004.01.008. [DOI] [PubMed] [Google Scholar]
  14. Dinzeo T, Cohen A, Nienow T, Docherty N. Arousability in schizophrenia: relationship to emotional and physiological reactivity and symptom severity. Acta Psychiatr Scand. 2008;117:432–439. doi: 10.1111/j.1600-0447.2008.01185.x. [DOI] [PubMed] [Google Scholar]
  15. Docherty NM, St-Hilaire A, Aakre JM, Seghers JP. Life events and high-trait reactivity together predict psychotic symptom increases in schizophrenia. Schizophr Bull. 2008;35:638–645. doi: 10.1093/schbul/sbn002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Donchin E, Coles MG. Is the P300 component a manifestation of context updating? Behav Brain Sci. 1988;11:357–374. [Google Scholar]
  17. Doucet C, Stelmack RM. The effect of response execution on P3 latency, reaction time, and movement time. Psychophysiology. 1999;36:351–363. doi: 10.1017/s0048577299980563. [DOI] [PubMed] [Google Scholar]
  18. Fachner J, Gold C, Erkkilä J. Music therapy modulates fronto-temporal activity in rest-EEG in depressed clients. Brain Topogr. 2013;26:338–354. doi: 10.1007/s10548-012-0254-x. [DOI] [PubMed] [Google Scholar]
  19. Feige B, Scheffler K, Esposito F, Di Salle F, Hennig J, Seifritz E. Cortical and subcortical correlates of electroencephalographic alpha rhythm modulation. J Neurophysiol. 2005;93(5):2864–2872. doi: 10.1152/jn.00721.2004. [DOI] [PubMed] [Google Scholar]
  20. Fenton G, Fenwick P, Dollimore J, Dunn T, Hirsch S. EEG spectral analysis in schizophrenia. Br J Psychiatry. 1980;136:445–455. doi: 10.1192/bjp.136.5.445. [DOI] [PubMed] [Google Scholar]
  21. Fioravanti M, Carlone O, Vitale B, Cinti ME, Clare L. A meta-analysis of cognitive deficits in adults with a diagnosis of schizophrenia. Neuropsychol Rev. 2005;15:73–95. doi: 10.1007/s11065-005-6254-9. [DOI] [PubMed] [Google Scholar]
  22. Fredrickson BL. The broaden-and-build theory of positive emotions. Philos Trans R Soc B. 2004;359:1367. doi: 10.1098/rstb.2004.1512. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Gabrielsson A. Emotion perceived and emotion felt: same or different? Music Sci. 2001;5:123–147. [Google Scholar]
  24. Ganguli M, Ratcliff G, Chandra V, Sharma S, Gilby J, Pandav R, Belle S, Ryan C, Baker C, Seaberg E. A Hindi version of the MMSE: the development of a cognitive screening instrument for a largely illiterate rural elderly population in India. Int J Geriatr Psychiatry. 1995;10:367–377. [Google Scholar]
  25. George EM, Coch D. Music training and working memory: an ERP study. Neuropsychologia. 2011;49:1083–1094. doi: 10.1016/j.neuropsychologia.2011.02.001. [DOI] [PubMed] [Google Scholar]
  26. Geretsegger M, Mössler KA, Bieleninik Ł, Chen XJ, Heldal TO, Gold C. Music therapy for people with schizophrenia and schizophrenia-like disorders. Cochrane Database Syst Rev. 2017:28553702. doi: 10.1002/14651858.CD004025.pub4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gjerde PF. Attentional capacity dysfunction and arousal in schizophrenia. Psychol Bull. 1983;93:57–72. [PubMed] [Google Scholar]
  28. Glicksohn J, Steinbach I, Elimalach-Malmilyan S. Cognitive dedifferentiation in eidetics and synaesthesia: hunting for the ghost once more. Perception. 1999;28:109–120. doi: 10.1068/p2755. [DOI] [PubMed] [Google Scholar]
  29. Grace AA, Gomes FV. The circuitry of dopamine system regulation and its disruption in schizophrenia: insights into treatment and prevention. Schizophr Bull. 2018;45:148–157. doi: 10.1093/schbul/sbx199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Green MF. Cognitive impairment and functional outcome in schizophrenia and bipolar disorder. J Clin Psychiatry. 2006;67:e12. [PubMed] [Google Scholar]
  31. Green MF, Harvey PD. Cognition in schizophrenia: past, present, and future. Schizophr Res Cogn. 2014;1:e1–e9. doi: 10.1016/j.scog.2014.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Green MF, Kern RS, Robertson MJ, Sergi MJ, Kee KS. In: Cognition in Schizophrenia. Sharma T, Harvey PD, editors. Oxford University Press; 2000. Cognitive functioning, social adjustment and long-term outcome in schizophrenia; pp. 178–192. [Google Scholar]
  33. Gruzelier JH. Bilateral asymmetry of skin conductance orienting activity and levels in schizophrenics. Biol Psychol. 1973;1:21–41. doi: 10.1016/0301-0511(73)90011-2. [DOI] [PubMed] [Google Scholar]
  34. Gu R, Zhang J, Zhou J, Tong M. The baroque music’s influence on learning efficiency based on the research of brain cognition; PIERS Proceedings; 2014. [Google Scholar]
  35. Hallam S, Price J, Katsarou G. The effects of background music on primary school pupils’ task performance. Educ Stud. 2002;28:111–122. [Google Scholar]
  36. Harris JG, Minassian A, Perry W. Stability of attention deficits in schizophrenia. Schizophr Res. 2007;91:107–111. doi: 10.1016/j.schres.2006.12.021. [DOI] [PubMed] [Google Scholar]
  37. Hegde S. Music-based cognitive remediation therapy for patients with traumatic brain injury. Front Neurol. 2014;5:34. doi: 10.3389/fneur.2014.00034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Hegde S. Music therapy for mental disorder and mental health: the untapped potential of Indian classical music. B J Psych international. 2017;14:31–33. doi: 10.1192/s2056474000001732. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Hegde S, Aucouturier J-J, Ramanujam B, Bigand E. Variations in emotional experience during phases of elaboration of North Indian Raga performance; 12th International Conference on Music Perception and Cognition and the 8th Triennial Conference of the Cognitive Sciences of Music; 2012. pp. 412–413. [Google Scholar]
  40. Hegde S, Thirthalli J, Rao SL, Raguram A, Philip M, Gangadhar BN. Cognitive deficits and its relation with psychopathology and global functioning in first episode schizophrenia. Asian J Psychiatr. 2013;6:537–543. doi: 10.1016/j.ajp.2013.07.002. [DOI] [PubMed] [Google Scholar]
  41. Heinrichs RW, Miles AA, Ammari N, Muharib E. Cognitive Impairment in Schizophrenia: Characteristics, Assessment and Treatment. 2013. Cognition as a central illness feature in schizophrenia; pp. 1–23. [Google Scholar]
  42. Hsieh L-T, Ranganath C. Frontal midline theta oscillations during working memory maintenance and episodic encoding and retrieval. Neuroimage. 2014;85:721–729. doi: 10.1016/j.neuroimage.2013.08.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Hurless N, Mekic A, Peña S, Humphries E, Gentry H, Nichols D. Music genre preference and tempo alter alpha and beta waves in human non-musicians. Impulse. 2013:1–11. [Google Scholar]
  44. Ishii R, Shinosaki K, Ukai S, Inouye T, Ishihara T, Yoshimine T, Hirabuki N, Asada H, Kihara T, Robinson SE. Medial prefrontal cortex generates frontal midline theta rhythm. Neuroreport. 1999;10:675–679. doi: 10.1097/00001756-199903170-00003. [DOI] [PubMed] [Google Scholar]
  45. Jensen O, Tesche CD. Frontal theta activity in humans increases with memory load in a working memory task. Eur J Neurosci. 2002;15:1395–1399. doi: 10.1046/j.1460-9568.2002.01975.x. [DOI] [PubMed] [Google Scholar]
  46. Jeon YW, Polich J. Meta-analysis of P300 and schizophrenia: patients, paradigms, and practical implications. Psychophysiology. 2003;40:684–701. doi: 10.1111/1469-8986.00070. [DOI] [PubMed] [Google Scholar]
  47. Kayashima Y, Yamamuro K, Makinodan M, Nakanishi Y, Wanaka A, Kishimoto T. Effects of canon chord progression on brain activity and motivation are dependent on subjective feelings, not the chord progression per se. Neuropsychiatr Dis Treat. 2017;13:1499. doi: 10.2147/NDT.S136815. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev. 1999;29:169–195. doi: 10.1016/s0165-0173(98)00056-3. [DOI] [PubMed] [Google Scholar]
  49. Koelsch S. A neuroscientific perspective on music therapy. Ann N Y Acad Sci. 2009;1169:374–384. doi: 10.1111/j.1749-6632.2009.04592.x. [DOI] [PubMed] [Google Scholar]
  50. Koelsch S. Towards a neural basis of music-evoked emotions. Trends Cogn Sci. 2010;14:131–137. doi: 10.1016/j.tics.2010.01.002. [DOI] [PubMed] [Google Scholar]
  51. Kosugi N, Oshiyama C, Kodama N, Niwa S-I. Incorporating music therapy into cognitive remediation to improve both cognitive dysfunction and negative symptoms in schizophrenia. Schizophr Res. 2019;204:423. doi: 10.1016/j.schres.2018.08.012. [DOI] [PubMed] [Google Scholar]
  52. Kwon M, Gang M, Oh K. Effect of the group music therapy on brain wave, behavior, and cognitive function among patients with chronic schizophrenia. Asian Nurs Res. 2013;7:168–174. doi: 10.1016/j.anr.2013.09.005. [DOI] [PubMed] [Google Scholar]
  53. Lezak MD. Neuropsychological assessment in behavioral toxicology–developing techniques and interpretative issues. Scand J Work Environ Health. 1984;10:25–29. [PubMed] [Google Scholar]
  54. Lin Y-P, Wang C-H, Jung T-P, Wu T-L, Jeng S-K, Duann J-R, Chen J-H. EEG-based emotion recognition in music listening. IEEE Trans Biomed Eng. 2010;57:1798–1806. doi: 10.1109/TBME.2010.2048568. [DOI] [PubMed] [Google Scholar]
  55. Lu SF, Lo CH, Sung HC, Hsieh TC, Yu SC, Chang SC. Effects of group music intervention on psychiatric symptoms and depression in patient with schizophrenia. Complement Ther Med. 2013;21:682–688. doi: 10.1016/j.ctim.2013.09.002. [DOI] [PubMed] [Google Scholar]
  56. Mathalon DH, Hoffman RE, Watson TD, Miller RM, Roach BJ, Ford JM. Neurophysiological distinction between schizophrenia and schizoaffective disorder. Front Hum Neurosci. 2010;3:70. doi: 10.3389/neuro.09.070.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Morris R, Griffiths O, Le Pelley ME, Weickert TW. Attention to irrelevant cues is related to positive symptoms in schizophrenia. Schizophr Bull. 2013;39:575–582. doi: 10.1093/schbul/sbr192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Morstyn R, Duffy FH, Mccarley RW. Altered topography of EEG spectral content in schizophrenia. Electroencephalogr Clin Neurophysiol. 1983;56:263–271. doi: 10.1016/0013-4694(83)90251-1. [DOI] [PubMed] [Google Scholar]
  59. Nakamura M, Matsushima E, Ohta K, Ando K, Kojima T. Relationship between attention and arousal level in schizophrenia. Psychiatry Clin Neurosci. 2003;57:472–477. doi: 10.1046/j.1440-1819.2003.01150.x. [DOI] [PubMed] [Google Scholar]
  60. Nayar S. Bhatkhande’s Contribution to Music: A Historical Perspective. Popular Prakashan; 1989. [Google Scholar]
  61. Newson JJ, Thiagarajan TC. EEG frequency bands in psychiatric disorders: a review of resting state studies. Front Hum Neurosci. 2018;12:521. doi: 10.3389/fnhum.2018.00521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Niedermeyer E. The normal EEG of the waking adult. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. 2005;167:155–164. [Google Scholar]
  63. Nuechterlein KH. 1991. Vigilance in Schizophrenia and Related Disorders. [Google Scholar]
  64. Nuechterlein KH, Barch DM, Gold JM, Goldberg TE, Green MF, Heaton RK. Identification of separable cognitive factors in schizophrenia. Schizophr Res. 2004;72:29–39. doi: 10.1016/j.schres.2004.09.007. [DOI] [PubMed] [Google Scholar]
  65. Nuechterlein KH, Green MF, Calkins ME, Greenwood TA, Gur RE, Gur RC, Lazzeroni LC, Light GA, Radant AD, Seidman LJ, Siever LJ, et al. Attention/vigilance in schizophrenia: performance results from a large multi-site study of the Consortium on the Genetics of Schizophrenia (COGS) Schizophr Res. 2015;163:38–46. doi: 10.1016/j.schres.2015.01.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. O’connell RG, Dockree PM, Kelly SP. A supramodal accumulation-to-bound signal that determines perceptual decisions in humans. Nat Neurosci. 2012;15:1729. doi: 10.1038/nn.3248. [DOI] [PubMed] [Google Scholar]
  67. Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia. 1971;9:97–113. doi: 10.1016/0028-3932(71)90067-4. [DOI] [PubMed] [Google Scholar]
  68. Overall JE, Gorham DR. The brief psychiatric rating scale. Psychol Rep. 1962;10:799–812. [Google Scholar]
  69. Pelletier CL. The effect of music on decreasing arousal due to stress: a meta-analysis. J Music Ther. 2004;41:192–214. doi: 10.1093/jmt/41.3.192. [DOI] [PubMed] [Google Scholar]
  70. Peng SM, Koo M, Kuo JC. Effect of group music activity as an adjunctive therapy on psychotic symptoms in patients with acute schizophrenia. Arch Psychiatr Nurs. 2010;24:429–434. doi: 10.1016/j.apnu.2010.04.001. [DOI] [PubMed] [Google Scholar]
  71. Pizzagalli DA, Oakes TR, Davidson RJ. Coupling of theta activity and glucose metabolism in the human rostral anterior cingulate cortex: an EEG/PET study of normal and depressed subjects. Psychophysiology. 2003;40:939–949. doi: 10.1111/1469-8986.00112. [DOI] [PubMed] [Google Scholar]
  72. Polich J. Clinical application of the P300 event-related brain potential. Phys Med Rehabil Clin. 2004;15:133–161. doi: 10.1016/s1047-9651(03)00109-8. [DOI] [PubMed] [Google Scholar]
  73. Polich J. Updating P300: an integrative theory of P3a and P3b. Clin Neurophysiol. 2007;118:2128–2148. doi: 10.1016/j.clinph.2007.04.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Putkinen V, Makkonen T, Eerola T. Music-induced positive mood broadens the scope of auditory attention. Soc Cogn Affect Neurosci. 2017;12:1159–1168. doi: 10.1093/scan/nsx038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Rabelo CM, Neves-Lobo IF, Rocha-Muniz CN, Ubiali T, Schochat E. Cortical inhibition effect in musicians and non-musicians using P300 with and without contralateral stimulation. Braz J Otorhinolaryngol. 2015;81:63–70. doi: 10.1016/j.bjorl.2014.11.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Rickard NS. Intense emotional responses to music: a test of the physiological arousal hypothesis. Psychol Music. 2004;32:371–388. [Google Scholar]
  77. Saletu B, Küfferle B, Anderer P, Grünberger J, Steinberger K. EEG-brain mapping in schizophrenics with predominantly positive and negative symptoms: comparative studies with remoxipride/haloperidol. Eur Neuropsychopharmacol. 1990;1:27–36. doi: 10.1016/0924-977x(90)90007-w. [DOI] [PubMed] [Google Scholar]
  78. Salimpoor VN, Benovoy M, Longo G, Cooperstock JR, Zatorre RJ. The rewarding aspects of music listening are related to degree of emotional arousal. PLoS One. 2009;4:e7487. doi: 10.1371/journal.pone.0007487. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Salimpoor VN, Benovoy M, Larcher K, Dagher A, Zatorre RJ. Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nat Neurosci. 2011;14:257. doi: 10.1038/nn.2726. [DOI] [PubMed] [Google Scholar]
  80. Sammler D, Grigutsch M, Fritz T, Koelsch S. Music and emotion: electrophysiological correlates of the processing of pleasant and unpleasant music. Psychophysiology. 2007;44:293–304. doi: 10.1111/j.1469-8986.2007.00497.x. [DOI] [PubMed] [Google Scholar]
  81. Särkämö T, Tervaniemi M, Laitinen S, Forsblom A, Soinila S, Mikkonen M, Autti T, Silvennoinen HM, Erkkilä J, Laine M. Music listening enhances cognitive recovery and mood after middle cerebral artery stroke. Brain. 2008;131:866–876. doi: 10.1093/brain/awn013. [DOI] [PubMed] [Google Scholar]
  82. Schacter DL. EEG theta waves and psychological phenomena: a review and analysis. Biol Psychol. 1977;5:47–82. doi: 10.1016/0301-0511(77)90028-x. [DOI] [PubMed] [Google Scholar]
  83. Schellenberg EG, Weiss MW. The Psychology of Music. Third ed. Elsevier; 2013. Music and cognitive abilities; pp. 499–550. [Google Scholar]
  84. Schellenberg EG, Nakata T, Hunter PG, Tamoto S. Exposure to music and cognitive performance: tests of children and adults. Psychol Music. 2007;35:5–19. [Google Scholar]
  85. Schmidt B, Hanslmayr S. Resting frontal EEG alpha-asymmetry predicts the evaluation of affective musical stimuli. Neurosci Lett. 2009;460:237–240. doi: 10.1016/j.neulet.2009.05.068. [DOI] [PubMed] [Google Scholar]
  86. Schulz S, Bolz M, Bär K-J, Voss A. Central-and autonomic nervous system coupling in schizophrenia. Philos Trans R Soc A Math Phys Eng Sci. 2016;374:20150178. doi: 10.1098/rsta.2015.0178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples) Biometrika. 1965;52:591–611. [Google Scholar]
  88. Talwar N, Crawford MJ, Maratos A, Nur U, Mcdermott O, Procter S. Music therapy for in-patients with schizophrenia: exploratory randomised controlled trial. Br J Psychiatry. 2006;189:405–409. doi: 10.1192/bjp.bp.105.015073. [DOI] [PubMed] [Google Scholar]
  89. Terry PC, Curran M, Karageorghis CI. Does music really make a difference? Meta-analytic review of a century of research; Proceedings of the 28th International Congress of Applied Psychology (ICAP 2014); 2014. [Google Scholar]
  90. Thaut MH. In: Rhythm, Music and the Brain: Scientific Foundations and Clinical Applications. Thaut MH, editor. Routledge; New York: 2005. Neurologic music therapy in cognitive rehabilitation; pp. 179–202. [Google Scholar]
  91. Thompson WF, Schellenberg EG, Husain G. Arousal, mood, and the Mozart effect. Psychol Sci. 2001;12:248–251. doi: 10.1111/1467-9280.00345. [DOI] [PubMed] [Google Scholar]
  92. Tseng P-T, Chen Y-W, Lin P-Y, Tu K-Y, Wang H-Y, Cheng Y-S, Chang Y-C, Chang C-H, Chung W, Wu C-K. Significant treatment effect of adjunct music therapy to standard treatment on the positive, negative, and mood symptoms of schizophrenic patients: a meta-analysis. BMC Psychiatry. 2016;16:16. doi: 10.1186/s12888-016-0718-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Uhlhaas PJ. High-frequency oscillations in schizophrenia. Clin EEG Neurosci. 2011;42:77–82. doi: 10.1177/155005941104200208. [DOI] [PubMed] [Google Scholar]
  94. Ulrich G, Houtmans T, Gold C. The additional therapeutic effect of group music therapy for schizophrenic patients: a randomized study. Acta Psychiatr Scand. 2007;116:362–370. doi: 10.1111/j.1600-0447.2007.01073.x. [DOI] [PubMed] [Google Scholar]
  95. Venables P, Wing J. Level of arousal and the subclassification of schizophrenia. Arch Gen Psychiatry. 1962;7:114–119. doi: 10.1001/archpsyc.1962.01720020038006. [DOI] [PubMed] [Google Scholar]
  96. Yang C-Y, Chen C-H, Chu H, Chen W-C, Lee T-Y, Chen S-G, Chou K-R. The effect of music therapy on hospitalized psychiatric patients’ anxiety, finger temperature, and electroencephalography: a randomized clinical trial. Biol Res Nurs. 2012;14:197–206. doi: 10.1177/1099800411406258. [DOI] [PubMed] [Google Scholar]
  97. Zahn T. Autonomic reactivity and behavior in schizophrenia. Psychiatr Res Rep. 1964;19:156. [PubMed] [Google Scholar]
  98. Zahn TP, Carpenter WT, Mcglashan TH. Autonomic nervous system activity in acute schizophrenia: I. method and comparison with normal controls. Arch Gen Psychiatry. 1981;38:251–258. doi: 10.1001/archpsyc.1981.01780280019001. [DOI] [PubMed] [Google Scholar]
  99. Zakharova N, Ivashchenko O. Effect of music on motor reaction time and interhemispheric relations. Zhurnal vysshei nervnoi deiatelnosti imeni IP Pavlova. 1984;34:212–218. [PubMed] [Google Scholar]
  100. Zhu W-N, Zhang J-J, Liu H-W, Ding X-J, Ma Y-Y, Zhou C-L. Differential cognitive responses to guqin music and piano music in Chinese subjects: an event-related potential study. Neurosci Bull. 2008;24:21–28. doi: 10.1007/s12264-008-0928-2. [DOI] [PMC free article] [PubMed] [Google Scholar]

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